Skip to contents

Conditional density estimators

Source: R/density_estimator.R
density_estimator.Rd

A conditional density estimator learns \(q_\phi(\theta \mid x)\). In neuralsbi every estimator is trained in standardized space and exposes two generics:

Details

  • de_log_prob(de, theta, x) – log density of theta given x

  • de_sample(de, x, n) – draw n parameter vectors given a single x

The contract is really q(target | condition): it makes no assumption about which of the two arguments is the parameter. npe() calls it with theta as the target and x as the condition, learning the posterior. nle() swaps the two, learning the likelihood instead, with the same estimators and the same two generics.

Four estimators ship today:

  • "maf" – a Masked Autoregressive Flow (Papamakarios et al., 2017), a stack of invertible autoregressive transforms with an exact change-of-variables density. This is the default, matching Python sbi, and requires the torch back end.

  • "nsf" – a Neural Spline Flow (Durkan et al., 2019): the same autoregressive structure as the MAF, but with a monotonic rational-quadratic spline transform in place of MAF's affine one, which handles sharply non-Gaussian posteriors better. Requires torch.

  • "mdn" – a Mixture Density Network (neural network -> Gaussian mixture). Requires torch.

  • "linear_gaussian" – a closed-form conditional Gaussian baseline (least-squares mean, residual covariance). No neural network, no torch. It is exact for linear-Gaussian simulators and doubles as a fast baseline and a regression-test oracle.

References

Papamakarios, G., Pavlakou, T. and Murray, I. (2017). Masked Autoregressive Flow for Density Estimation. NeurIPS. doi:10.48550/arXiv.1705.07057

Durkan, C., Bekasov, A., Murray, I. and Papamakarios, G. (2019). Neural Spline Flows. NeurIPS. doi:10.48550/arXiv.1906.04032